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Identifying and Defining Business Problems

Lesson 5/52 | Study Time: 20 Min

Identifying and defining business problems is a critical first step in any analytics or improvement initiative.

Proper problem recognition helps organizations understand gaps between current performance and desired outcomes, enabling targeted actions for resolution.

Clear problem statements provide a foundation for focused analysis, ensuring that resources and efforts address the most impactful issues. 

Problem Recognition

Problem recognition involves observing discrepancies between how things are and how they ideally should be. It may arise through performance gaps, market feedback, customer complaints, or declining financial indicators.

These gaps highlight inefficiencies, lost opportunities, risk exposures, or unsatisfactory outcomes requiring investigation.


Examples of detected problems:


1. Sales declining below targets

2. Production delays causing missed deadlines

3. Increasing customer churn rates

4. Rising operational costs impacting profitability


Recognizing a problem early allows organizations to address issues before they escalate, preserving competitive advantage and stakeholder confidence.

Problem Categorization

Once problems are recognized, categorizing them helps prioritize and tailor analyses. Common categories include:


1. Cost Optimization: Identifying inefficiencies to reduce expenses without sacrificing quality or output.

2. Revenue Improvement: Exploring ways to increase sales, customer acquisition, or average transaction value.

3. Risk Mitigation: Understanding vulnerabilities and uncertainties to minimize potential losses or compliance issues.

4. Efficiency Enhancement: Streamlining processes, reducing waste, and improving operational workflows.

This categorization informs the selection of appropriate analytical methods and resource allocation for maximal business impact.

Clarifying Ambiguous Problem Statements and Scope Management

Ambiguously defined problems can misdirect resources and cause analytic efforts to be unfocused or ineffective. Clear problem articulation and scope management are essential to maintaining alignment between analytics and business objectives.


Effective scope control prevents project creep and supports timely, actionable results.

Techniques for Problem Articulation

Five Whys Analysis: This iterative technique involves asking "why" five times (or until the root cause is found), moving beyond symptoms to uncover fundamental problems.


For example:

Problem: Customer orders are delayed.


Why? Because production lines are frequently halted.

Why? Because equipment failures occur often.

Why? Because maintenance schedules are irregular.

Why? Because the maintenance team is understaffed.

Why? Because of budget cuts.


Addressing the root cause (understaffing) is more effective than just fixing equipment temporarily.

Root Cause Identification: Beyond the Five Whys, root cause analysis systematically investigates all plausible causes through data collection, cause-and-effect diagrams (Fishbone diagrams), and process mapping. It ensures comprehensive understanding and prevents misdiagnosis.

Evan Brooks

Evan Brooks

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Class Sessions

1- Introduction to Business Analytics 2- Types of Business Analytics 3- Analytics Frameworks and Problem-Solving Approaches 4- Analytics Career Path and Professional Skills 5- Identifying and Defining Business Problems 6- Analytical Context and Business Alignment 7- SMART Objectives and Success Metrics 8- Stakeholder Engagement and Decision Framework 9- Introduction to Databases and SQL Fundamentals 10- Data Retrieval and Query Writing 11- Data Preparation and Cleaning 12- Data Organization and Transformation 13- Descriptive Statistics 14- Data Visualization Fundamentals 15- Probability Concepts for Business 16- Sampling and Data Collection Methods 17- Hypothesis Testing Framework 18- Statistical Tests for Business Applications 19- Real-World Business Applications of Hypothesis Testing 20- Confidence Intervals and Decision-Making 21- Excel Functions and Formulas 22- Pivot Tables and Advanced Reporting 23- Data Modeling and Analysis Tools 24- Scenario Analysis and Optimization 25- Data Visualization Principles and Design 26- Storytelling with Data 27- Tool Proficiency: Tableau and Power BI 28- Executive Communication and Presentation 29- Customer Analytics Fundamentals 30- Market Segmentation Strategies 31- Churn Analysis and Retention Modeling 32- Personalization and Customer Experience Optimization 33- Operational Analytics Framework 34- Demand Forecasting and Inventory Management 35- Supply Chain Optimization 36- Simulation and What-If Analysis 37- Fundamentals of Predictive Modeling 38- Regression Analysis for Forecasting 39- Time Series Forecasting 40- Business Applications of Predictive Modeling 41- Machine Learning Fundamentals 42- Classification Models 43- Real-World Machine Learning Applications 44- Machine Learning Considerations for Business 45- Financial Data Analysis 46- Cost Analysis and Optimization 47- Pricing Analytics 48- Investment and Risk Analysis 49- Project Scope and Problem Definition 50- End-to-End Analytics Workflow 51- Business Recommendation Development 52- Professional Presentation and Communication

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